code: def test_tcn_separator_output():
      x = torch.rand(2, 10, 10)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      for num_spk in range(1, 3):
          model = TCNSeparator(
              input_dim=10,
              layer=num_spk,
              stack=2,
              bottleneck_dim=3,
              hidden_dim=3,
              kernel=3,
              causal=False,
          )
          model.eval()
          specs, _, others = model(x, x_lens)
          assert isinstance(specs, list)
          assert isinstance(others, dict)
          for n in range(num_spk):
              assert "mask_spk{}".format(n + 1) in others
              assert (
                  specs[n].shape
                  == others["mask_spk{}".format(n + 1)].shape
              )
  
file_path: espnet/test/espnet2/enh/separator/test_tcn_separator.py
imports: import pytest
  import torch
  from torch import Tensor
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.tcn_separator import TCNSeparator
label_desc: Use image node_id 4 for calling a global function with example usage: test_tcn_separator_output() without return types